Search Results

This paper explores a two-step inertial derivative-free projection method with a relaxation factor γ ∈ ( 0 , 2 ) for solving nonlinear pseudo-monotone equations. Unlike existing inertial algorithms for the system of nonlinear pseudo-monotone ...

This work focuses on India’s tallest heap of garbage at Ghazipur landfill site in New Delhi. About 2000 tonnes of garbage is being dumped at Ghazipur every day and it causes poisonous gases. Fire, sparked by methane gas coming from the garbage and ...

Synthetic aperture radar (SAR) classification models based on convolutional neural networks have high accuracy, but the models’ security is still threatened by adversarial examples. The high threat of adversarial examples derives from the ...

• Detect adversarial examples for SAR images by automatic feature attribution.
• Design feature attribution scan block adaptively via dual-objective optimization.
• Balance the number of subsamples and AUC value by evolution of NSGA-II.

In recent years, waste sorting has become a key focus in urban environmental management worldwide, and its effectiveness depends on advanced classification algorithms and practical deployment technologies. However, due to the limited computational power ...

A cluster cum classification hierarchical bit map assisted (CCHBMA) medium access control (MAC) protocol is proposed for the energy-efficient data transmission of wireless sensor networks. The proposed protocol utilizes the advantages of ...

This paper aims to improve the accuracy and privacy protection of credit card fraud detection within the E-commerce domain through differential privacy algorithms. First, we introduce the particular dataset under study with its preprocessing steps. Then ...

Today’s Information Technology landscape is rapidly evolving. Cyber professionals are increasingly concerned about maintaining security and privacy. Research has shown that the emergence of new malware is on the rise. The realm of malware assault ...

Active Learning has been a popular method to circumvent the labeling cost in machine learning methods. The majority of active learning approaches can be classified into two categories: representative-based and informative-based methods, with some ...

• A general optimization framework for a wide range of query strategies.
• Extended label complexity to measure the performance of active learning.
• The naïve Similarity-Based Active Learning query strategy.

Metaheuristics provide efficient approaches for many combinatorial problems. Research focused on improving the performance of metaheuristics has increasingly relied on either combining different metaheuristics, or leveraging methods that ...

• Learning methods can enhance the effectiveness/efficiency of tabu search.
• Proposed learning methods guide the search process of tabu search.
• Learning algorithms are able to identify the most promising regions.

The advanced learning paradigm, learning using privileged information (LUPI), leverages information in training that is not present at the time of prediction. In this study, we developed privileged logistic regression (PLR) models under the LUPI ...

• Presented the first LUPI-based privileged logistic regression model and extended it for cases with partial privileged information.
• Performed an asymptotic analysis to determine when adding privileged information improves the ...

Logistic regression is a well-known method for classification and is being widely used in our daily life. To obtain a logistic regression model with sufficient accuracy, collecting a large number of data samples from multiple sources is ...

In the distributed machine learning field, federated learning (FL) serves as a highly effective framework for dismantling data silos and integrating data from multiple sources. However, the flourishing of FL still encounters significant ...

• A fine-grained Cryptography-TEE hybrid security model for multi-party cooperative computation scenario is defined.
• A series of TEE-assisted secure computation protocols are designed to achieve secure and lossless non-linear operations.

To reduce the estimator's variance and prevent overfitting, regularization techniques have attracted great interest from the statistics and machine learning communities. Most existing regularized methods rely on the sparsity assumption that a ...

• Propose a novel regularization method for classification problems.
• Provide theoretical results, including consistency of the proposed estimator.
• Provide extensive simulation and empirical experimental results, which support the ...

• The research gap is addressed in terms of sustainability and quality by design.
• A modified distance-related procedure is proposed to generate the design matrix.
• A logistic regression technique is developed for modeling responses.

Quality by design (QbD) focuses on satisfying the needs of products or processes when aiming for variance reduction. On the other side, sustainability is associated with carrying on to conduct so over time. The research gap is also addressed in ...

In this paper, an accelerated spectral conjugate gradient projection algorithm is proposed for solving the constrained nonlinear pseudo-monotone equations. We set a restart procedure related to the conjugate parameter in the search direction of ...

Over the decade, DevOps practices have gained popularity within software development organizations for managing the dynamic behavior of system development. Implementing DevOps introduces risks and challenges that increase the difficulties in ...

• To investigate the most significant features of developing DevOps project in software development organizations.
• An empirical validation of the research model using responses collected from the 126 DevOps software practitioners.
• ...

Temporal information plays an important role in student performance prediction, so two interpretable feature factors, namely enthusiasm and stability, are extracted from online and offline blended temporal learning data, which can characterize ...

In recent years, machine learning techniques have been widely deployed in various fields. However, machine learning faces problems like high computation overhead, low training accuracy, and poor security due to data silos, privacy issues and ...

• We propose a PPMLR scheme that supports authentication.
• Our scheme is fault tolerant and does not need the data owner to interact with the cloud server.
• Our scheme is enabled to create highly accurate models because it does not ...

• Application of Machine Learning models in customer backorder prediction.
• Explore the trade-off between complexity and number of predictors to improve prediction accuracy.
• Using simpler models with fewer attributes is preferable to ...

The complexity of global supply chains, with their multi-tiered and lengthy structures, presents significant challenges for effectively planning inventory replenishment, accurately forecasting demand, and managing customer backorders. To maintain ...

Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL, there are several assumptions for the relationships and methods to ...